code: def convert_to_xywh(boxes):
      xmin, ymin, xmax, ymax = boxes.unbind(1)
      return torch.stack((xmin, ymin, xmax - xmin, ymax - ymin), dim=1)
  
imports: import copy
  import io
  from collections import OrderedDict
  from contextlib import redirect_stdout
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import pycocotools.mask
  import torch
  import torch.distributed
  from pycocotools.coco import COCO
  from pycocotools.cocoeval import COCOeval
  from avalanche.evaluation.metrics.detection import DetectionEvaluator, TCommonDetectionOutput
label_desc: Use image node_id 1 for calling a global function with example usage: convert_to_xywh(boxes) and returns: torch